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Psych 156A/ Ling 150: Acquisition of Language II

Psych 156A/ Ling 150: Acquisition of Language II. Lecture 11 Phrases. Announcements. HW2 due today at the end of class Review questions posted for phrases HW3 available (due 5/29/12). About Language Structure. Sentences are not just strings of words. The girl danced with the elven king.

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Psych 156A/ Ling 150: Acquisition of Language II

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  1. Psych 156A/ Ling 150:Acquisition of Language II Lecture 11 Phrases

  2. Announcements HW2 due today at the end of class Review questions posted for phrases HW3 available (due 5/29/12)

  3. About Language Structure Sentences are not just strings of words. The girl danced with the elven king.

  4. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammatical category. Noun (N) = girl, goblin, dream, laughter, … Determiner (Det) = a, the, an, these, … Adjective (Adj) = lovely, stinky, purple, … Verb (V) = laugh, dance, see, defeat, … Adverb (Adv) = lazily, well, rather, … Preposition (P) = with, on, around, towards, …

  5. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammatical category. Det N V P Det Adj N The girl danced with the elven king.

  6. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Noun Phrases (NP)

  7. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Noun Phrases (NP) Can be replaced with pronouns like “he”, “she”, “it”, …

  8. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammatical category. Det N V P Det Adj N She danced with him. Noun Phrases (NP) Can be replaced with pronouns like “he”, “she”, “it”, …

  9. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Preposition Phrases (PP)

  10. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Preposition Phrases (PP) Can be replaced with words like “here” and “there”

  11. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced there. Preposition Phrases (PP) Can be replaced with words like “here” and “there”

  12. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammatical category. Det N V P Det Adj N The girl danced with the elven king. Verb Phrases (VP)

  13. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Verb Phrases (VP) Can be replaced with words like “do so” and “did so”

  14. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl didso. Verb Phrases (VP) Can be replaced with words like “do so” and “did so”

  15. About Language Structure Sentences are not just strings of words. Words cluster into larger units called phrases, based on their grammaticalcategory. Det N V P Det Adj N The girl danced with the elven king. Verb Phrases (VP) Preposition Phrases (PP) Noun Phrases (NP)

  16. About Language Structure Sentences are not just strings of words. Sentence Another way to represent this phrase structure VP PP NP NP Det N V P Det Adj N The girl danced with the elven king.

  17. Computational Problem How do children figure out which words belong together (as phrases) and which words don’t? Det N V P Det Adj N The girl danced with the elven king. Det N V P Det Adj N The girl danced with the elven king.

  18. Learning Phrases One way we’ve seen that children can learn things is by tracking the statistical information available. Saffran, Aslin, & Newport (1996): Transitional Probability is something 8-month-olds can track Prob(“stlebe”) < Prob(“castle”) Prob(“stlebe”) < Prob(“beyond”) to the castle beyond the goblin city Posit a word boundary at the minimum of the transitional probabilities between syllables

  19. Learning Phrases One way we’ve seen that children can learn things is by tracking the statistical information available. Thompson & Newport (2007): Transitional Probability used to divide words into phrases? the girl and the dwarf… Posit a phrase boundary where the transitional probability is low between words (=~ group words together when their transitional probability is high)?

  20. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild.

  21. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild. If the child only ever sees this order of categories, there’s no way to know how the words break up into phrases using transitional probabilities. Why? TrProb(AB) = TrProb(BC) = TrProb(CD) = TrProb(DE) = TrProb(EF) = 1 ABCDEF

  22. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild. But suppose C is an optional word/phrase. (easily is an adverb that can be left out) ABCDEF ABDEF Data without C sometimes will appear. Thegoblinstealsthechild.

  23. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild. With the optional phrase left out, TrProb(BC) is less than 1 since sometimes B is followed by D instead of always being followed by C. A transitional probability learner later encountering ABCDEF might posit a phrase boundary between B and C because Tr(AB) and TrProb(CD) are still 1. ABCDEF ABDEF Thegoblinstealsthechild.

  24. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild. Conclusion: ABis a unit, CDEFis a unit. thegoblin (= NP) easilystealsthechild (= VP) ABCDEF ABDEF Thegoblinstealsthechild.

  25. A look at real language properties in action with transitional probabilities Example: Optional phrases A BC DEF Thegoblineasilystealsthechild. For ABDEF, Tr(AB) and Tr(DE) = 1, but TrProb(BD) < 1. So, a transitional probability learner will posit a boundary between B and D. Conclusion: ABis a unit, DEFis a unit. thegoblin (= NP) stealsthechild (= VP) ABCDEF ABDEF Thegoblinstealsthechild.

  26. Artificial Language Experiments Thompson & Newport 2007: Adults (not children) listened to data from an artificial language for 20 minutes on multiple days Assumption: Adults who are learning an artificial language will behave like children who are learning their first language since the adults have no prior experience with the artificial just as children have no prior experience with their first language Is this a good assumption to make?

  27. Adults in Artificial Language Experiments = Children in First Language? Maybe yes, if children’s brains behave like adults’ brains. Then, the fact that adults can learn phrases from transitional probabilities means children should also be able to learn phrases from transitional probabilities. Maybe no, if there are other factors that could interfere, such as adults having more cognitive resources to process information or using their native language experience to help them learn something about the artificial language. Then, just because adults succeed doesn’t mean children will also succeed.

  28. Some evidence that adults and children differ Hudson Kam & Newport (2005): Adults and 5- to 7-year-old children differ in their willingness to make generalizations. Adults and children were presented with an artificial language that used determiners (words like “the” and “a” in English) inconsistently in noun phrases. Sometimes, the determiner would appear (maybe 40%, 60% or 75% of the time) and sometimes it wouldn’t. Example of inconsistent use in English (rather than an artificial language): “I want the pirate to win.” “I want pirate to win.”

  29. Some evidence that adults and children differ Hudson Kam & Newport (2005): Adults and 5- to 7-year-old children differ in their willingness to make generalizations. When presented with inconsistent input, adult learners matched the input and did not generalize determiner usage to all noun phrases. So, if they heard a determiner 60% of the time, they used a determiner 60% of the time when they produced sentences in this language. Adult production: “I want the pirate to win.” (60%) “I want pirate to win.” (40%)

  30. Some evidence that adults and children differ Hudson Kam & Newport (2005): Adults and 5- to 7-year-old children differ in their willingness to make generalizations. When presented with inconsistent input, child learners often generalized determiner usage to all noun phrases. So, if they heard a determiner 60% of the time, they used a determiner either 100% of the time when they produced sentences in this language - or 0% of the time (they didn’t generalize the right way necessarily). Child production: “I want the pirate to win.” (100%) “I want pirate to win.” (0%)

  31. …but maybe not as much as we think Hudson Kam & Newport (2009): Adults can be made to generalize too, when given inconsistent input. When presented with inconsistent input but with one determiner being dominant (used 60% of the time as compared to others used 20% or less of the time), adult learners often generalizedonly the dominant determiner and used it nearly all the time (90%). Adult production: “I want the pirate to win.” (90%) “I want pirate to win.” (10%)

  32. …but maybe not as much as we think Hudson Kam & Newport (2009): Children still differ from adults in what they generalize. When presented with inconsistent input but with one determiner being dominant (used 60% of the time as compared to others used 20% or less of the time), child learners often generalizedone determiner (even if it wasn’t the dominant one) and used it nearly all the time (ex: 90%). Child production: “I want pirate to win.” (10%) “I want thispirate to win.” (90%)

  33. Artificial Language Similar To Real Language? Properties of the artificial language that are similar to real language properties optional phrases (the goblinchaseda chickenin the castle) PP is optional in the sentence repeated phrases (NPVerbNPPP) More than one NP is used in the sentence moved phrases (In the castle the goblin chased a chicken) PP is moved to the front of the sentence

  34. Artificial Language Experiments Baseline pattern: ABCDEF real language parallel A BCDEF Thegoblineasilystealsachild. Artificial Language Phrases ABCDEF

  35. How do we tell if learning happened? Baseline assessment: Can subjects actually realize all these nonsense words belong to 6 distinct categories? Can they categorize? kofhoxjessotfalker is the same as daznebtidzorrudsib

  36. How do we tell if learning happened? Baseline assessment: Can subjects actually realize all these nonsense words belong to 6 distinct categories? Can they categorize? See if they can tell the difference between the correct order they were exposed to (ABCDEF) and some other pattern they never heard (ABCDCF) kofhoxjessotfalker is the same as daznebtidzorrudsib kofhoxjessotfalker is right kofhoxjessotrelkeris wrong

  37. How do we tell if learning happened? Phrase learning assessment: If they can categorize, do they learn what the phrases are (AB,CD,EF)? Example: test between ABand non-phrase BC Sample test item - which one do they think belongs together? kofhox vs. hoxjes

  38. Learning a language with optional phrases Baseline pattern: ABCDEF Other patterns heard (phrases ABCDEFmissing): CDEF, ABEF, ABCD kofhoxjessotfalker relzortafnav mernebrudsib dazlevtidlum Control subjects: Control language (remove one adjacent pair at a time) Additional control patterns heard: BCDE, ABCF, ADEF

  39. Learning a language with optional phrases Transitional Probabilities in the Optional Phrase language and the Control language are different. The Optional Phrase language has lower probability across phrase boundaries than within phrases. The control language has the same probability no matter what.

  40. Learning a language with repeated phrases Baseline pattern: ABCDEF Other patterns heard (phrases ABCDEFrepeated): ABCDEFAB, ABCDEFCD, ABCDEFEF kofhoxjessotfalker kofhoxrelzortafnavdazneb mernebjeszorrudsibtidsot dazlevtidlumfalnavtafker Control subjects: Control language (repeat one adjacent pair at a time) Additional control patterns heard: ABCDEFBC, ABCDEFDE, ABCDEFAF

  41. Learning a language with repeated phrases Transitional Probabilities in the Repeated Phrase language and the Control language are different. The Repeated Phrase language has lower probability across phrase boundaries than within phrases. The control language has almost the same probability no matter what.

  42. Learning a language with moved phrases Baseline pattern: ABCDEF Other patterns heard (phrases ABCDEFmoved): ABEFCD, CDABEF, CDEFAB, EFABCD, EFCDAB Example strings heard: kofhoxjessotfalker daznebtafnavrelzor … Control subjects: Control language (move one adjacent pair at a time) Additional control patterns heard: BCAFDE, AFDEBC, DEAFBC, DEBCAF

  43. Learning a language with moved phrases Transitional Probabilities in the Moved Phrase language and the Control language are different. The Moved Phrase language has lower probability across phrase boundaries than within phrases. The control language has the same probability no matter what.

  44. Artificial Language Learning: Categorization, Day 1 Generally above chance performance (50%), control group performing about the same or a little worse than test groups.

  45. Artificial Language Learning: Categorization, Day 5 General improvement, though test groups still a little better than control groups. Still, subjects generally capable of categorization. Mean % correct for all subjects is significantly above chance (which would be 50%)

  46. Artificial Language Learning: Phrases, Day 1 In each case, even after only 20 minutes of exposure (day 1), test subjects are better than control subjects for each of the languages with optional, repeated, or moved phrases.

  47. Artificial Language Learning: Phrases, Day 5 After 5 days of exposure (100 minutes), the difference between control subjects and test subjects becomes apparent. control?? Human tendency towards binary groupings

  48. Artificial Language Learning: Phrases, Day 5 After 5 days of exposure (100 minutes), the difference between control subjects and test subjects becomes apparent. control?? Human tendency towards binary groupings Some properties seem easier to pick up on than others (repeated and movement language subjects are much better than control subjects).

  49. Artificial Language Learning: Phrases, Day 5 After 5 days of exposure (100 minutes), the difference between control subjects and test subjects becomes apparent. control?? Human tendency towards binary groupings Interestingly, control subjects in the optional phrase condition actually did really well - this is unexpected since the transitional probabilities were uninformative.

  50. Learning a language with optional phrases, repeated phrases, and moved phrases Baseline pattern: ABCDEF Other patterns heard (phrases ABCDEFmoved, repeated, or left out): CDEF, ABEF, ABCD, ABCDEFAB, ABCDEFCD, ABCDEFEF, ABCDEF, ABEFCD, CDABEF, CDEFAB, EFABCD, EFCDAB Transitional Probabilities in the “All-combined” language and the Control language are different. The “All-combined” language has lower probability across phrase boundaries than within phrases. The control language probabilities are more uniform, though they do vary.

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